Search Results (121)

This study presents the design, development, and evaluation of an integrated solar-powered seed sowing and fertilizer-watering system to enhance planting efficiency, improve resource utilization, and reduce labor in small-scale agriculture. The prototype features a 600-watt photovoltaic panel, DC motors, and a manual mechanical dispensing mechanism, enabling automated seed placement, water distribution, and fertilizer application in off-grid farm environments. Development was guided by a product-based design approach using locally sourced materials to ensure cost-effectiveness, maintainability, and accessibility for rural users. Field simulations and performance trials assessed charging efficiency, seed sowing accuracy, irrigation flow rate, and fertilizer dispensing precision. Results showed high consistency in operational performance, including up to 99% seed placement accuracy, efficient water delivery, and reliable fertilizer timing, with solar energy providing adequate power storage during periods of peak irradiance. Expert evaluations using a standardized instrument demonstrated strong agreement on the system’s usability, material availability, ergonomic features, modularity, and overall functional design. Findings indicate that the system can minimize manual labor, reduce operational costs, and offer a practical transition toward clean-energy–assisted mechanization in agriculture. The study concludes that integrating renewable energy into essential farm operations can contribute to sustainable productivity and recommends future enhancements through sensor integration, increased battery capacity, and adaptive control mechanisms to support wider agricultural adoption. Full article

This research introduces a unique optimization framework centered on the Geese V-Formation Algorithm to enhance the technical planning of distributed energy resources in renewable microgrid-oriented radial distribution systems. The proposed methodology addresses the optimal placement and sizing of photovoltaic panels, wind turbines, battery energy storage systems, and capacitor banks to provide comprehensive voltage support, minimize active power losses, and refine overall grid functionality. Drawing inspiration from the aerodynamic efficiency of migratory geese, the Geese V-Formation Algorithm integrates dynamic leader-follower coordination, adaptive role rotation, and cooperative information exchange mechanisms. These features allow the algorithm to effectively balance global exploration and local exploitation, making it uniquely suited to address the complex, nonlinear, and multi-objective nature of modern microgrid design. The effectiveness of this approach was evaluated through rigorous simulations on the IEEE-33 and IEEE-69 bus distribution systems utilizing the Python programming language. The empirical results indicate that the Geese V-Formation Algorithm achieves substantial power loss reductions, reaching 91.62% and 92.45%, respectively, when integrating solar and wind resources with energy storage and reactive power compensation. Furthermore, the optimized configurations significantly improved bus voltage profiles and enhanced substation power factors, confirming the technical effectiveness of the framework under the considered benchmark constraints. By providing a technical decision-support approach for engineers and utility planners, this framework facilitates the deployment of reliable, decentralized renewable energy systems that align with global energy transition objectives and promote sustainable infrastructure development. Full article

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. The methodology is applied to the IEEE 33-bus radial distribution test system, where the initial operating condition is characterized in terms of nodal voltage profile, voltage deviation, voltage-stability index, active-power losses, and annual loss cost. The optimization process identifies buses 13 and 31 as the most suitable locations for two identical BESS units, with the reported validation case evaluating each unit at upper admissible capacity limits of $1000\mathrm{kW}$ and $4000\mathrm{kWh}$. The obtained results show that the optimized BESS allocation increases the minimum voltage profile to values above $0.94\mathrm{p}.\mathrm{u}.$, raises the voltage-stability index to more than 0.88, reduces active-power losses to approximately $0.0166\mathrm{p}.\mathrm{u}.$, and decreases the annual cost associated with active-power losses by more than $66\%$ relative to the base case. Additional validation through sensitivity analysis, repeated stochastic runs, operating-mode evaluation, and comparison against a genetic algorithm confirms the consistency and robustness of the proposed ACO-based methodology. The results demonstrate that the proposed framework provides a technically consistent and computationally accessible solution for improving voltage regulation, reducing feeder losses, and lowering loss-related operating costs in radial distribution systems. Full article

The displacement of synchronous generators by inverter-based renewable energy sources (RES) has eroded system inertia, weakening frequency stability even as voltage stability improves. This paradox poses a major challenge for modern grids. Battery Energy Storage Systems (BESS) offer synthetic inertia and rapid frequency response, but their stabilising impact depends critically on placement. Using dynamic simulations on the IEEE 9-bus system, this study demonstrates the voltage–frequency paradox across increasing RES penetration. Results show that strategic siting prevents frequency collapse while enhancing voltage recovery, providing a unified mitigation strategy for high-renewable systems. Full article

Containerized mobile power supplies (CMPS), a critical energy replenishment carrier for all-electric ships, have caused severe economic losses via frequent fire and explosion accidents during temporary port storage in recent years. Existing literature focuses on battery thermal runaway under laboratory conditions and maritime transport risk analysis, but its conclusions are not directly applicable to port temporary storage. Port storage, featuring full-charge quiescent placement and high turnover, differs significantly from maritime transport, while its high-temperature and humid environment is distinct from laboratory settings. Furthermore, no system safety-based risk assessment framework exists, failing to deliver targeted mitigation strategies for practical operations. To address these issues, fault tree analysis (FTA), Bayesian network (BN), and attack–defense game theory were combined to build a systematic safety risk assessment framework. FTA clarified the hazard factors’ correlation mechanism; based on FTA, BN conducted a quantitative evaluation. Extended from BN results, attack–defense game theory identified key risk evolution paths and formulated targeted prevention and control measures. The main conclusions are as follows: Combined with similar accident features and port storage scenario attributes, internal correlations between hazard-inducing factors were clarified via FTA. Based on expert evaluations and BN calculation, the target port’s fire accident occurrence probability was determined as 2.41%, with two core root nodes identified via sensitivity analysis. Two critical risk evolution paths corresponding to IE1 (thermal runaway initiation) and IE2 (failure of protection and emergency response systems) were identified via game theory and traversal method, with occurrence probabilities of 1.50% and 1.77%, respectively. Targeted prevention and control measures adapted to the port storage scenario were proposed based on path triggering mechanisms. These findings provide theoretical support for port enterprises to improve CMPS fire prevention and emergency response capabilities, elevate port safety management levels, and promote the safe development of the all-electric vessel shipping industry. Full article

As global energy demands rise and concerns about environmental sustainability intensify, renewable energy sources like solar photovoltaic (PV) systems have gained significant attention. An integrated approach is proposed, leveraging spatial analysis using Helioscope, a 3D solar design tool, incorporated with Geographic Information System (GIS) data. This study conducted a spatial analysis of Cape Peninsula University of Technology (CPUT) Bellville campus’s potential for renewable energy, and the results are promising. The research indicated that the campus has enough rooftop space to optimally place solar panels with a capacity of 7.8 megawatts, which is more than the campus’s total energy needs of 6.3 megawatts. This study identified 13,249 modules that can be optimally placed to achieve this. Full article

The increasing penetration of converter-interfaced renewable energy sources has led to reduced system inertia and increased frequency stability challenges in modern power systems. Battery energy storage systems (BESSs) provide fast active power support. However, their effectiveness depends on the installation location, power rating, and network characteristics. This paper proposes a power-flow-informed, sensitivity-based method for the optimal placement and sizing of distributed BESSs to improve the frequency nadir and rate of change of frequency (RoCoF). The method integrates marginal frequency sensitivity obtained from time-domain simulations with network coupling information derived from power-flow analysis within a constrained optimization framework solved using particle swarm optimization. The network coupling weight, derived from voltage sensitivity, represents the steady-state electrical connectivity and active power redistribution capability, rather than transient frequency dynamics. It is used in combination with frequency sensitivity to improve the discrimination of candidate buses. The method is evaluated on the IEEE 39-bus system under multiple generator outage contingencies. For the most severe contingency (G01), the baseline system exhibits a frequency nadir of 55.9230 Hz and an RoCoF of 0.2404 Hz/s. With the proposed method, the frequency nadir improves to 58.6561 Hz, corresponding to an increase of 2.7330 Hz (4.88%), while the RoCoF is reduced to 0.1224 Hz/s (49.17% reduction). The optimal solution distributes a total BESS capacity of 298 MW across multiple buses, with the largest allocation of 46 MW at Bus 36. Across additional contingencies, the proposed method consistently achieves higher frequency nadirs and lower RoCoFs compared with both the baseline system and benchmark placement methods. The results demonstrate that combining dynamic frequency sensitivity with power-flow-based network coupling provides a physically consistent and computationally efficient strategy for distributed BESS allocation in low-inertia power systems. Full article

Smart insoles equipped with pressure sensor matrices are increasingly used for gait analysis, yet high-density arrays compromise battery life and data throughput. This study aims to identify the optimal sparse sensor layout required to accurately estimate the Center of Pressure (CoP) by analyzing the trade-off between sensor number, spatial placement, and reconstruction error. Plantar pressure data were collected from twelve healthy participants walking at a self-selected speed using 16-sensor connected insoles. A combinatorial algorithm evaluated all $2^{16}-1$ possible sensor combinations to minimize the Root Mean Square Error (RMSE) in the antero-posterior, medio-lateral, and global Euclidean directions. Results reveal a non-linear convergence of accuracy that depends on the spatial axis. For longitudinal and global progression, a clear inflection point achieving sub-centimetric accuracy (RMSE < 5 mm) is reached at seven sensors. In contrast, medio-lateral tracking shows its largest discrete error reduction at five sensors, followed by gradual improvements at higher densities. Anatomical frequency analysis highlights distinct spatial requirements: the posterior heel is consistently selected for medio-lateral accuracy, while the lateral arch and metatarsal regions are critical for longitudinal progression. These findings suggest that while a minimum of seven strategically placed sensors enables robust CoP tracking across all spatial axes, optimal hardware design should remain task-specific. This work provides a data-driven framework for the development of energy-efficient wearable gait monitoring systems. Full article

A replicable methodology for testing the size and placement of Battery Energy Storage Systems connected to high-voltage transmission networks is presented in this study. The proposed approach involves the power flow analysis inside a Renewable Energy Zone, namely a high-renewable area prone to grid congestion during peak generation periods, based on time-series hourly analysis over a critical month. The model includes detailed operational descriptions such as lines ampacity, battery state of charge limits, round-trip efficiency, self-discharge behavior, and ramp rate restrictions. The methodology distinguishes itself by its simplicity, flexibility, and use of open-source tools, making it a valuable asset for supporting future transmission planning in high-renewable-energy scenarios. The model was developed in Python (version 3.12) using the open-source Pandapower library, introducing an innovative constraint management criterion, and validated against real data provided by the national Transmission System Operator. The approach was then applied to a portion of the Sicilian grid with massive wind and solar penetration. Results show that strategic allocation of batteries leads to a significant reduction in line overloads (up to 13 GWh mitigated in one month), improves the dispatch of renewable energy generated within the Renewable Energy Zone and allows a more sustainable exercise of the power system. Full article

The decarbonization of the transportation sector necessitates the adoption of practical measures that can be implemented within existing fleets. One such measure is the installation of solar panels on trucks, which has shown potential to reduce fuel consumption in heavy-duty vehicles (HDVs). This study presents lessons learned from a monitoring project involving 200 commercial trucks retrofitted with 300–500 W solar panels, aimed at supplementing battery charging and minimizing alternator operation. The system incorporated commercially available flexible photovoltaic (PV) modules, adhesive mounting techniques, a charge controller, and a data logger housed within a control box. Documentation covered installation procedures, wiring practices, and safety considerations across various truck models, with additional insights from electrical contractors regarding labor time and costs. Results indicate that adhesive-based mounting can be carried out safely and reliably without structural modifications, although wiring and control box placement constitute the most significant portions of the installation process. The project further identified variability in installation duration and economic viability, depending on vehicle configuration and technician expertise. Overall, the findings affirm that vehicle-integrated photovoltaic (VIPV) retrofits are both technically feasible and operationally robust. They also underscore the practical requirements, constraints, and workforce considerations essential for scaling deployment within commercial fleets. Full article

The increasing penetration of renewable energy sources reduces system inertia and introduces significant challenges for maintaining frequency stability in modern power grids. Battery Energy Storage Systems (BESS) have emerged as an effective solution for mitigating frequency deviations; however, existing studies typically recommend relocating BESS to the bus that is electrically furthest from the Center of Inertia (COI) to maximize frequency support. This paper investigates an alternative operational strategy in which the BESS remains co-located with the renewable energy source. A methodology combining COI-based electrical distance analysis and an artificial intelligence (AI)-driven dispatch framework is proposed to evaluate optimal BESS utilization without physical relocation. The AI model generates generator dispatch scenarios that are evaluated through dynamic simulations to assess the resulting system frequency nadir following disturbances. The proposed approach is validated using a modified IEEE nine-bus power system model. Simulation results demonstrate that, under specific generator dispatch conditions, maintaining the BESS at the renewable energy bus can achieve frequency-nadir performance comparable to relocating the BESS to the furthest bus from the COI. The analysis further identifies critical generator output ranges that influence frequency stability under different BESS placement scenarios. These findings suggest that optimized dispatch strategies can reduce the need for costly infrastructure relocation while maintaining effective frequency support in low-inertia power systems. Full article

This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems (BSSs), wind DGs, shunt capacitors (SCs) and electric vehicle charging stations (EVCSs). With the rapid adoption of electric vehicles as part of global decarbonization efforts, integrating EVCSs into already stressed distribution networks poses significant operational challenges, often requiring system reinforcement supported by renewable-based DGs. The uncoordinated deployment of EVCSs and DGs can exacerbate power losses and deteriorate voltage profiles. To address these issues, the first stage of the methodology employs GSA to optimally allocate solar DGs with BSSs, wind DGs and SCs, targeting objectives such as minimizing power losses, enhancing voltage stability and alleviating substation loading. The second stage identifies optimal locations and maximum feasible capacities for EVCS integration. Finally, the third stage upgrades the network to mitigate the impacts of EVCS integration. The effectiveness of the proposed approach is validated through simulations on a practical 52-bus, 11 kV distribution network under hourly varying load, solar irradiance and wind velocity conditions for all seasons. The simulation results show an 85% reduction in power losses during peak hours, with nodal voltages maintained above 0.95 p.u. under all scenarios. Additionally, net-zero grid power exchange during peak periods confirms the full islanded operation. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

As a promising alternative for cleaner vehicles, the growth of Battery Electric Vehicle (BEV) adoption should be supported by a reliable charging infrastructure. Therefore, projecting the charging load is required to ensure that the electricity supply is adequate as BEV adoption increases. This study proposes a multistage approach for projecting BEV charging load demand, linking a One-dimensional Convolutional Neural Network (1D-CNN) forecasting model with BEV users’ travel behavior analysis to perform spatiotemporal agent-based trip and charging simulations, which model various types of BEVs traveling across multiple regions. The 1D-CNN model achieves high performance with an RMSE of 0.073 and an R² of 0.881, providing a 10-year BEV adoption outlook. The empirical study in nine regions of Greater Jakarta, Indonesia, shows the one-week temporal charging load demand for three milestone periods—2025, 2030, and 2035—exploring weekday and weekend demand, as well as home and public charging demand at points of interest (POIs). This study identifies a difference between aggregate charging load demand and per-vehicle load intensity: the aggregate demand concentration occurs in South Jakarta (21% for public charging and 22% for home charging), while the highest per-vehicle spatial concentration ratio occurs in Depok (36% for public charging and 16% for home charging) due to long-distance travel patterns. The distribution of charging demand at the subdistrict level provides a basis for charging infrastructure placement, transformer sizing, and charging tariff design. Full article

Because of their great energy density and efficiency, lithium-ion batteries (LIBs) are essential to renewable energy systems and electric vehicles. Effective battery management requires precise estimation of the state of health (SoH) and state of charge (SoC). In order to overcome the difficulties caused by parameter fluctuations and real-world disturbances, this work presents a novel μ-analysis-based methodology designed to improve the resilience and accuracy of online SoC and SoH estimations in LIBs. In contrast to conventional techniques, the suggested strategy successfully manages both structured and unstructured uncertainties in battery systems by combining μ-analysis with model-based estimation. The framework creates an estimator that is resistant to parameter drift and outside perturbations by combining model-based estimation approaches with μ-analysis tools. Simulations using UDDS, US06, and HWFET driving cycles are used to verify its performance. When evaluating battery health and condition in dynamic and uncertain operating scenarios, the μ-analysis-based estimator demonstrates superior accuracy compared to conventional H∞-pole placement filter methods. The proposed approach enhances system robustness, achieving an 8 dB improvement in disturbance attenuation, as verified through MATLAB/Simulink. Stability analysis reveals the μ-analysis controller maintains robust performance up to ‖∆‖∞ = 3.5 at 10 Hz, compared to only ‖∆‖∞ = 1.5 for the H∞-pole placement controller—demonstrating significantly greater tolerance to parameter variations and unmodeled dynamics. These capabilities make the μ-analysis approach particularly suitable for electric vehicle applications requiring next-generation battery management systems. Full article